The universal speech attributes for speaker verification (SV)
are addressed in this paper. The aim of this work is to
exploit fundamental characteristics across different speakers
within the deep neural network (DNN)/i-vector framework.
The manner and place of articulation form the fundamental
speech attribute unit inventory, and new attribute units for
acoustic modelling are generated by a two-step automatic
clustering method in this paper. The DNN based on
universal attribute units is used to generate posterior
probability in total variability modelling and i-vector
extracting for the speaker recognition procedure.
Furthermore, Gaussian mixture models (GMMs) are used to
fit the distribution of the features associated with a given
context-dependent attribute unit to improve performance.
The experiments are carried out on the core test from the
NIST SRE 2008 corpus; the proposed system can obtain
better performance than all other state-of-the-art systems.